delete_MNAR_rank: Create MNAR values using a ranking mechanism

In missMethods: Methods for Missing Data

Create MNAR values using a ranking mechanism

Description

Create missing not at random (MNAR) values using a ranking mechanism in a data frame or a matrix

Usage

delete_MNAR_rank(
  ds,
  p,
  cols_mis,
  n_mis_stochastic = FALSE,
  ties.method = "average",
  miss_cols
)

Arguments

ds	A data frame or matrix in which missing values will be created.
p	A numeric vector with length one or equal to length cols_mis; the probability that a value is missing.
cols_mis	A vector of column names or indices of columns in which missing values will be created.
n_mis_stochastic	Logical, should the number of missing values be stochastic? If n_mis_stochastic = TRUE, the number of missing values for a column with missing values cols_mis[i] is a random variable with expected value nrow(ds) * p[i]. If n_mis_stochastic = FALSE, the number of missing values will be deterministic. Normally, the number of missing values for a column with missing values cols_mis[i] is round(nrow(ds) * p[i]). Possible deviations from this value, if any exists, are documented in Details.
ties.method	How ties are handled. Passed to rank.
miss_cols	Deprecated, use cols_mis instead.

Details

The functions delete_MNAR_rank and delete_MAR_rank are sisters. The only difference between these two functions is the column that controls the generation of missing values. In delete_MAR_rank a separate column cols_ctrl[i] controls the generation of missing values in cols_mis[i]. In contrast, in delete_MNAR_rank the generation of missing values in cols_mis[i] is controlled by cols_mis[i] itself. All other aspects are identical for both functions. Therefore, further details can be found in delete_MAR_rank.

Value

An object of the same class as ds with missing values.

References

Santos, M. S., Pereira, R. C., Costa, A. F., Soares, J. P., Santos, J., & Abreu, P. H. (2019). Generating Synthetic Missing Data: A Review by Missing Mechanism. IEEE Access, 7, 11651-11667

See Also

delete_MAR_rank

Other functions to create MNAR: delete_MNAR_1_to_x(), delete_MNAR_censoring(), delete_MNAR_one_group()

Examples

ds <- data.frame(X = 1:20, Y = 101:120)
delete_MNAR_rank(ds, 0.2, "X")

missMethods documentation built on Sept. 16, 2022, 5:08 p.m.